Bottom Line:
This approach enabled us to analyse female behaviour during the testing period, and the resulting paternity success and fitness consequences of a given choice.We show that genetic incompatibilities arising from the t haplotype had severe indirect fitness consequences and t females avoided fertilization by t incompatible males.The results are inconclusive whether this avoidance of t fertilization by t females was caused by pre- or post-copulatory processes.

Mentions:
The results of the paternity analyses were used to estimate fertilization bias, that is the proportion of offspring sired by the t male given female genotype i,Fi ∈ [0,1]. Any systematic deviation from no preference (Fi = 0.5) can be the result of pre- or post-copulatory mate choice. Hence, we were interested in two pieces of information: (i) The mean prediction indicates whether there is a systematic deviation from no choice expectations (H0: Fi = 0.5). This information will not help to distinguish between pre- and post-copulatory choice processes. For this, we need to estimate (ii) variance (see below for more details). However, a binomial generalized linear model (GLM) – the standard approach for this type of data – will not estimate the variance (because binomial errors directly follow from binomial parameters p and n). Therefore, we ran three custom models using computer simulations. Each model assumed a specific fertilization probability (see below), for which we derived the expected probability distribution of Fi (see Fig. 3b). From these expected distributions, we drew 105 values based on our sample sizes (nt = 19, nw = 15), giving us a population of realized mean E[Fi] and variance V[Fi] expectations. These expectations were compared to observed mean and variance . Here is a short description of the three models.

Mentions:
The results of the paternity analyses were used to estimate fertilization bias, that is the proportion of offspring sired by the t male given female genotype i,Fi ∈ [0,1]. Any systematic deviation from no preference (Fi = 0.5) can be the result of pre- or post-copulatory mate choice. Hence, we were interested in two pieces of information: (i) The mean prediction indicates whether there is a systematic deviation from no choice expectations (H0: Fi = 0.5). This information will not help to distinguish between pre- and post-copulatory choice processes. For this, we need to estimate (ii) variance (see below for more details). However, a binomial generalized linear model (GLM) – the standard approach for this type of data – will not estimate the variance (because binomial errors directly follow from binomial parameters p and n). Therefore, we ran three custom models using computer simulations. Each model assumed a specific fertilization probability (see below), for which we derived the expected probability distribution of Fi (see Fig. 3b). From these expected distributions, we drew 105 values based on our sample sizes (nt = 19, nw = 15), giving us a population of realized mean E[Fi] and variance V[Fi] expectations. These expectations were compared to observed mean and variance . Here is a short description of the three models.

Bottom Line:
This approach enabled us to analyse female behaviour during the testing period, and the resulting paternity success and fitness consequences of a given choice.We show that genetic incompatibilities arising from the t haplotype had severe indirect fitness consequences and t females avoided fertilization by t incompatible males.The results are inconclusive whether this avoidance of t fertilization by t females was caused by pre- or post-copulatory processes.